Classification of Text and Non-Text from Bilingual Real-Time Document Using Deep Learning Approach-Reference-Cited by-同舟云学术

Classification of Text and Non-Text from Bilingual Real-Time Document Using Deep Learning Approach

Published:2022-10-13 Issue: Volume: Page:
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Author:

G SHIVAKUMAR¹,M Ravikumar¹,J Shivaprasad B²

Affiliation:

1. Kuvempu University

2. Srinivasa Institute of Technology

Abstract

Abstract In this work, we have presented an efficient approach for classification of text and non-text document information from real time office documents images which are bilingual using a deep learning approach i.e., U-net architecture for experimentation purpose. We have created our own dataset containing 2000 document images. Initially pre-processing is applied on the input document images proposed method is compared with other existing methods and obtained accuracy of 99.62% different performance measure i.e., (Specificity, Sensitivity, Precision, F1-Score) used in the experimentation.

Publisher

Research Square Platform LLC

Reference66 articles.

1. C.P. Chaithanya, N. Manohar, Ajay Bazil Issac, Automatic Text Detection and Classification in Natural Images, International Journal of Recent Technology and Engineering (IJRTE), Volume-7, Issue-5S3, pp. 176–180, 2019.

2. Separation of text and non-text in document layout analysis using a recursive filter;Tran TA;KSII Transactions on Internet and Information Systems (TIIS),2015

3. Arvind, K. R., Pati, P. B., & Ramakrishnan, A. G. (2006). Automatic text block separation in document images. In 2006 Fourth International Conference on Intelligent Sensing and Information Processing (pp. 53–58). IEEE.

4. Text/non-text separation from handwritten document images using LBP based features: An empirical study;Ghosh S;Journal of Imaging,2018

5. Puri, S., & Singh, S. P. (2016, January). Text recognition in bilingual machine printed image documents—Challenges and survey: A review on principal and crucial concerns of text extraction in bilingual printed images. In 2016 10th International Conference on Intelligent Systems and Control (ISCO) (pp. 1–8). IEEE.